Introduction

This is a hindcast test of the EcoVerse - a suite of algorithms to reduce bycatch while maximizing target catch in near real-time. The hindcast was run between 1997-10-01 and 1997-11-31 and 2005-08-01 and 2005-11-31 in conjunction, two time periods for which there are historical observer and tracking records for each species in the EcoVerse. Records were subampled to have 280 records per species

Additionally, a random point hindcast was conducted, consisting of 1400 random points between 1997-10-01 and 1997-11-31 and 2005-08-01 and 2005-11-31. At each random point, all algorithm values were compared to all species habitat suitabilities.

THIS RMARKDOWN USES TWO NEW FUNCTIONS

Histograms of predicted habitat suitability at species presences

How the Marxan algorithm works
Marxan attempts to solve a min set cover problem, i.e. what is the minimum set of planning units (here 10x10 pixels) needed to meet targets for conservation features while minimizing costs.
-targets: the bycatch species weightings
-conservation features: the bycatch species
-costs: avoided swordfish

How Marxan is run in EcoMarxan
Species habitat suitability layers HSL are input into Marxan with the three bycatch species as conservation features and swordfish as a cost. The bycatch species weightings used to set targets for the conservation features (e.g. blsh = .4 —> protect 40% of blsh habitat). The swordfish weighting is used to set a penalty for failing to meet targets for the conservation features, e.g. when swor is high, the penalty is low, therefor we get a less conservative (in terms of avoiding bycatch) solution. For a given day, Marxan is run 1000 times to create an output of selection frequency, i.e. the number of times / 1000 each pixel was selected for a solution.

Four algorithms were tested:
1. EcoROMS - species habitat suitability layers (HSL) are weighted, summed, and then normalized between -1 and 1
2. Marxan raw - the raw selection frequency output (0-1000) rescaled to (-1,1)
3. Marxan mosaic - remove marxan pixels selected in < 100 solutions, rescale remaining pixels between -1 and 0, where -1= highly selected marxan pixels (e.g. most important for avoiding bycatch); 0=infrequently selected marxan pixels (e.g. least important for avoiding bycatch), fill in removed areas w raw swordfish values from HSL (scaled between 0,1)
4. Marxan mosaic 01 - remove marxan pixels selected in < 100 solutions, rescale between -1 and 0, where -1= highly selected marxan pixels (e.g. most important for avoiding bycatch); 0=infrequently selected marxan pixels (e.g. least important for avoiding bycatch), fill in removed areas w swordfish values (unscaled), rescale whole thing between -1,1

Run 1 - “generic weightings” - raw data

The generic EcoROMS weightings (-0.1,-0.1,-0.05,-0.9,0.9), and Marxan weightings that produce similar outputs (-0.1,-0.1,-0.05,-0.2,0.1).
namesrisk<-c(“Blue shark bycatch”,“Blue sharks”,“Sea lions”,“Leatherbacks”,“Swordfish”)

Example alorithm solutions

From 2005-08-01

Box plots

Algorithm values at historical records

Histograms by species

Swordfish

Leatherbacks

Maps by species

Swordfish

Leatherbacks

Run 2 - “SWOR and LBST at their most extremes” - raw data

Weightings in this run were select to seperate swordfish and leatherbacks as much as possible, keeping all other species weightings constant with Run 1. EcoROMS weightings: -0.1,-0.1,-0.05,-1.5,2 ; and Marxan weightings: -0.1,-0.1,-0.05,-0.3,0.6
namesrisk<-c(“Blue shark bycatch”,“Blue sharks”,“Sea lions”,“Leatherbacks”,“Swordfish”)

Example alorithm solutions

From 2005-08-01

Box plots

Algorithm values at historical records

Histograms by species

Swordfish

Leatherbacks

Maps by species

Swordfish

Leatherbacks

Run 3 - “extreme LBST, neutral SWOR” - raw data

Weightings in this run were select to seperate swordfish and leatherbacks as much as possible, keeping all other species weightings constant with Run 1. EcoROMS weightings: -0.1,-0.1,-0.05,-1.5,.1 ; and Marxan weightings: -0.1,-0.1,-0.05,-0.3,0.1
namesrisk<-c(“Blue shark bycatch”,“Blue sharks”,“Sea lions”,“Leatherbacks”,“Swordfish”)

Example alorithm solutions

From 2005-08-01

Box plots

Algorithm values at historical records

Histograms by species

Swordfish

Leatherbacks

Maps by species

Swordfish

Leatherbacks

Run 1 - “generic weightings” - random data

The generic EcoROMS weightings (-0.1,-0.1,-0.05,-0.9,0.9), and Marxan weightings that produce similar outputs (-0.1,-0.1,-0.05,-0.2,0.1).
namesrisk<-c(“Blue shark bycatch”,“Blue sharks”,“Sea lions”,“Leatherbacks”,“Swordfish”)

Example alorithm solutions

From 2005-08-01

Point clouds

Habitat suitability layers vs algorithm solutions

Run 2 - “SWOR and LBST at their most extremes” - random data

Weightings in this run were select to seperate swordfish and leatherbacks as much as possible, keeping all other species weightings constant with Run 1. EcoROMS weightings: -0.1,-0.1,-0.05,-1.5,2 ; and Marxan weightings: -0.1,-0.1,-0.05,-0.3,0.6
namesrisk<-c(“Blue shark bycatch”,“Blue sharks”,“Sea lions”,“Leatherbacks”,“Swordfish”)

Example alorithm solutions

From 2005-08-01

Point clouds

Habitat suitability layers vs algorithm solutions

Run 3 - “extreme LBST, neutral SWOR” - random data

Weightings in this run were select to seperate swordfish and leatherbacks as much as possible, keeping all other species weightings constant with Run 1. EcoROMS weightings: -0.1,-0.1,-0.05,-1.5,.1 ; and Marxan weightings: -0.1,-0.1,-0.05,-0.3,0.1
namesrisk<-c(“Blue shark bycatch”,“Blue sharks”,“Sea lions”,“Leatherbacks”,“Swordfish”)

Example alorithm solutions

From 2005-08-01

Point clouds

Habitat suitability layers vs algorithm solutions